Project - California Housing Price Prediction¶
DESCRIPTION¶
Problem Statement¶
- The US Census Bureau has published California Census Data which has 10 types of metrics such as the population, median income, median housing price, and so on for each block group in California. The dataset also serves as an input for project scoping and tries to specify the functional and nonfunctional requirements for it.
Goal¶
- The project aims at building a model of housing prices to predict median house values in California using the provided dataset. This model should learn from the data and be able to predict the median housing price in any district, given all the other metrics.
Data Snapshot¶
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Image('californiascreenshot.png')
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import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression,Ridge,Lasso
from sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score
from statsmodels.formula.api import ols
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.svm import SVR
from sklearn.decomposition import PCA
import xgboost as xgb
from IPython.display import Image
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
import reverse_geocoder as rg
import folium
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dt=pd.read_csv('C:\\Users\\Subhasish Das\\Desktop\\SimplyLearn\\Project\\Kaggle\\California_Housing_Price_Prediction\\housing.csv')
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dt.head()
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dt.shape
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dt.dtypes
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dt.describe()
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Feature-Engineering¶
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dt.hist(bins=50,figsize=(20,15))
plt.show()
Checking Null Values¶
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dt.isnull().sum()
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dt.total_bedrooms.unique()
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ax=dt.total_bedrooms.hist(bins=40)
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mean_bedrooms=dt.total_bedrooms.mean()
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mean_bedrooms
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median_bedrooms=dt.total_bedrooms.median()
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median_bedrooms
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dt.fillna(median_bedrooms,axis=1,inplace=True)
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dt.isnull().sum()
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sns.pairplot(dt)
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plt.figure(figsize=(10,10))
sns.heatmap(dt.corr(),annot=True,cmap='coolwarm')
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def reverseGeocode(coordinates):
result = rg.search(coordinates)
return (result)
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if __name__=="__main__":
# Coordinates tuple.Can contain more than one pair.
coordinates =list(zip(dt['latitude'],dt['longitude'])) # generates pair of (lat,long)
data = reverseGeocode(coordinates)
dt['name'] = [i['name'] for i in data]
dt['admin1'] = [i['admin1'] for i in data]
dt['admin2'] = [i['admin2'] for i in data]
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dt.head()
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dt.rename(columns={'name':'City','admin1':'State','admin':'County'},inplace=True)
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latitude = 37.88
longitude = -122.23
traffic_map = folium.Map(location=[latitude, longitude], zoom_start=5)
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colordict = {0: 'lightblue', 1: 'lightgreen', 2: 'orange', 3: 'red'}
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for latitude, longitude, City,median_house_value in zip(dt['latitude'], dt['longitude'], dt['City'], dt['median_house_value']):
folium.CircleMarker(
[latitude, longitude],
popup = ('City: ' + str(City).capitalize() + '<br>'
'median_house_value: ' + str(median_house_value)
),
color='b',
fill=True,
fill_opacity=0.7
).add_to(traffic_map)
display(traffic_map)